Daniel Romero

Daniel Romero


I started my career as a sysadmin long ago, configuring and maintaining bare-metal Linux servers. Then I closely followed the adoption of virtualization in servers and, later, the containerization process.

Over the years, I acquired new skills to work as a software engineer, starting with PHP, Java, and then Ruby. I’ve kept myself updated and unlocked new skills, working with Terraform, Ansible, Docker, and Kubernetes. I can comfortably work with AWS, Google Cloud, and Azure.

Now, I have started a new journey, exploring the fields of Data Science and Machine Learning.

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Data Scientist/Machine Learning Engineer
Apr 2021 – Present Remote

I worked to improve the data quality and helped with the automation of ML model training as well as the observability of the models. I participated in projects developing Machine Learning models to solve classification and regression problems with Scikit-Learn and deep learning problems with TensorFlow.

With the data science team, I applied and generalized statistical models to large datasets with Python, Scikit-Learn, and a lot of SQL. Other activities I got involved in were: Construction of production pipelines for ML using Airflow, Kubeflow, and SageMaker (implementing continuous training).

Senior Site Reliability Engineer
Sep 2018 – Apr 2021 Remote
As an SRE, my main job was to provide a low latency response to incidents and service instability at many cloud providers (AWS, GCP, Azure) on the cloud team. Besides, I contributed to the tools, automation, and system engineering efforts using Ansible, Terraform, Docker, Python, and Golang.
Loom Network
DevOps Engineer
Apr 2018 – Sep 2018 Remote
I worked helping to create and maintain a scalable infrastructure for Blockchains and DAppChains written in Go, with Docker, Kubernetes, and Ansible. Also, I helped to build the observability platform with Prometheus and Grafana.
Site Reliability Engineer
Mar 2017 – Apr 2018 Remote

I was responsible for designing and migrating the top Ruby on Rails applications previously on AWS to Google Cloud, using Docker and Kubernetes (GKE). During the migration, I worked with eBPF for the first time and applied performance profiling for some applications.

The automatic deploy pipeline that did not exist before was implemented using Codeship and Google Cloud Build. As a result of this work, it was possible to guarantee an immutable and scalable infrastructure.